Brain
Contents
Brain#
The results for the CNeuroMod quantitative MRI longitudinal stability study for the brain are displayed below in two sections, Quantitative MRI and Diffusion, reflecting the two separate pipelines used to processes these datasets (see the Materials and Methods section for pipeline diagrams). The mean intrasubject and intersubject COVs are reported in tables below each respective figure, as well as the intrasubject COV standard deviation.
The figures are presented in an interactive format using the Plotly framework, you can hover to view the values of each datapoints, use the dropdown box (when applicable) to change between metrics, click and drag to zoom in, etc.
This page was generated using an Jupyter Notebook, and all the commands run to reproduce the figure using the prepared and packaged ROI data are shown prior to the figures. If you’d like to re-run the notebook, you can click the 🚀 icon on the top right of this page and then on “Binder” to open a MyBinder session in your browser - no installation is required.
Quantitative MRI#
Code imports#
# Python imports
from IPython.display import clear_output
from pathlib import Path
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', 1000)
pd.set_option('display.colheader_justify', 'center')
pd.set_option('display.precision', 1)
# Import custom tools
from tools.data import Data
from tools.plot import Plot
from tools.stats import Stats
Download data#
data_type = 'brain'
release_version = 'latest'
dataset = Data(data_type)
dataset.download(release_version)
--2023-05-03 01:45:26-- https://github.com/courtois-neuromod/anat-processing/releases/download/r20220921/neuromod-anat-brain-qmri.zip
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Load data plot it#
qMRI Metrics#
dataset.load()
fig_gm = Plot(dataset, plot_name = 'brain-1')
fig_gm.title = 'Brain qMRI microstructure measures'
# If you're running this notebook in a Jupyter Notebook (eg, on MyBinder), change 'jupyter-book' to 'notebook'
fig_gm.display('jupyter-book')
Statistics#
White Matter#
stats_wm = Stats(dataset)
stats_wm.build_df('WM')
stats_wm.build_stats_table()
display(stats_wm.stats_table)
| T1 (MP2RAGE) | T1 (MTsat) | MTR | MTsat | |
|---|---|---|---|---|
| intrasubject COV mean [%] | 0.6 | 2.3 | 0.6 | 1.7 |
| intrasubject COV std [%] | 0.2 | 0.8 | 0.1 | 0.5 |
| intersubject mean COV [%] | 1.9 | 3.5 | 0.4 | 2.2 |
Grey Matter#
stats_gm = Stats(dataset)
stats_gm.build_df('GM')
stats_gm.build_stats_table()
display(stats_gm.stats_table)
| T1 (MP2RAGE) | T1 (MTsat) | MTR | MTsat | |
|---|---|---|---|---|
| intrasubject COV mean [%] | 0.4 | 3.1 | 0.8 | 2.7 |
| intrasubject COV std [%] | 0.1 | 1.6 | 0.2 | 1.2 |
| intersubject mean COV [%] | 1.0 | 5.7 | 1.2 | 4.5 |
Diffusion#
data_type = 'brain-diffusion-cc'
release_version = 'latest'
dataset = Data(data_type)
dataset.download(release_version)
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dataset.load()
fig_diff = Plot(dataset, plot_name = 'brain-diff-cc')
fig_diff.title = 'Brain diffusion measures (corpus callosum)'
# If you're running this notebook in a Jupyter Notebook (eg, on MyBinder), change 'jupyter-book' to 'notebook'
fig_diff.display('jupyter-book')
Statistics#
Genu#
stats_cc1 = Stats(dataset)
stats_cc1.build_df('genu')
stats_cc1.build_stats_table()
display(stats_cc1.stats_table)
| FA (DWI) | MD (DWI) | RD (DWI) | |
|---|---|---|---|
| intrasubject COV mean [%] | 0.8 | 1.0 | 1.3 |
| intrasubject COV std [%] | 0.3 | 0.6 | 0.6 |
| intersubject mean COV [%] | 4.2 | 6.2 | 10.3 |
Body#
stats_cc1 = Stats(dataset)
stats_cc1.build_df('body')
stats_cc1.build_stats_table()
display(stats_cc1.stats_table)
| FA (DWI) | MD (DWI) | RD (DWI) | |
|---|---|---|---|
| intrasubject COV mean [%] | 0.6 | 0.7 | 0.7 |
| intrasubject COV std [%] | 0.2 | 0.2 | 0.3 |
| intersubject mean COV [%] | 3.8 | 3.0 | 6.2 |
Splenium#
stats_cc1 = Stats(dataset)
stats_cc1.build_df('splenium')
stats_cc1.build_stats_table()
display(stats_cc1.stats_table)
| FA (DWI) | MD (DWI) | RD (DWI) | |
|---|---|---|---|
| intrasubject COV mean [%] | 0.6 | 0.7 | 0.8 |
| intrasubject COV std [%] | 0.1 | 0.2 | 0.3 |
| intersubject mean COV [%] | 2.6 | 3.1 | 6.3 |